Moreover, the energy landscape forms the foundation of the energy-structure-function maps that have in recent years demonstrated great power in materials discovery . By associating computed properties (e.g. gas uptake, charge carrier mobility) with hypothetical crystal structures from the CSP landscape, it becomes possible to predict whether a molecule is a promising candidate for creating new functional materials, i.e. if it has one (or more) favourably-ranked crystal structures which are predicted to achieve the desired property. The techniques and challenges in the field of organic CSP have been reviewed ; we provide a brief overview for the sake of context. CSP is typically considered a combination of two broad challenges: efficient and thorough sampling of the configuration space of crystal packing, and accurate, cost-effective structural optimisation, ranking, and property calculation. The sampling of hypothetical crystal packing arrangements is made extremely difficult due to the "curse of dimensionality"-the number of independent degrees of freedom to sample creates a vast configurational space. As a result, simple grid-based sampling approaches must be eschewed in favour of more sophisticated techniques, such as low-discrepancy quasi-random sampling and genetic algorithm approaches. Meanwhile, the optimisation and ranking methods must be accurate enough to describe the fine balance of different intermolecular interactions (electrostatics, dispersion, hydrogenbonding, etc.), resolving lattice energy differences often smaller than a kJ mol -1 , while sufficiently cost-effective to be applied to very large numbers (»10 5 ) of trial crystal structures. Historically, this has entailed the use of simple empirical force fields, but modern developments often employ tailor-made force fields or machine-learned potentials derived from ab initio calculations. Still, empirical force fields retain their power even today due to their efficiency and broad transferability, often being the initial step of a hierarchy of increasingly accurate (and expensive) energy models employed in one CSP workflow. Despite these two broad and ongoing challenges, organic CSP has demonstrated enormous success in diverse applications, crucially proving itself to be a truly predictive technique, guiding synthesis and discovery of novel forms of porous materials , highly-flexible pharmaceutical molecules , co-crystals , simple molecules previously thought to be monomorphic and templating of predicted metastable polymorphs.(34) A recurrent landmark in the field is the series of Cambridge Blind Tests of CSP, showcasing the diversity of methods (and success rates thereof) employed within different CSP techniques to predict experimental crystal structures without any knowledge beyond the molecular chemical diagram . Recent iterations of the Blind Test have demonstrated that CSP method development is successfully keeping apace with the complexity of molecules and crystal structures specifically selected to stress-test it. Some of the most consequential recent developments in CSP have employed machine learning (ML) techniques in one form or another . Among the most intuitive applications is the use of ML to learn relationships between the structure and lattice energy, either by learning the difference (i.e. ∆-ML) between lattice energies computed at a lower level of theory to those at a higher level, or to learn the relationship between the lattice energy and the ML descriptors directly through the training of ML potentials . These approaches have achieved high accuracy predictions at a fraction of the cost of the full periodic density functional theory (DFT) reference calculations -particularly significant given the latter's ubiquity in recent Blind Test entries. That said, ML techniques have further applications in CSP beyond improved optimisations and energy rankings. In particular, ML and related approaches applied to databases of experimental crystal structures and properties have demonstrated success in predicting NMR chemical shifts and in formulating models of molecular hydrogen bond propensities within crystals . Recent work by has demonstrated machine-learning of the relationship between crystal lattice energies and the relative contributions to these by different chemical functional groups, paving the way for data-driven insights into new crystal engineering techniques. ML also been shown to have potential to enhance the analysis of crystal energy landscapes, by identifying structurefunction relationships that might evade simple inspection but nevertheless offer explanatory and predictive power . Our aims in this work are threefold. Firstly, we seek to demonstrate the capability of our method for efficient, largescale rigid-molecule organic CSP by presenting the results of the largest to-date CSP study, applying the methods to over 1000 molecules with observed crystal structures in the CSD. Secondly, we in turn assess the quality and reliability of our method by evaluating how often the experimental crystal structures of these molecules are reproduced in our CSPs, and how well they are ranked energetically compared to hypothetical structures on their CSP landscapes. Finally, we demonstrate example applications of this dataset, including assessing the distribution of CSP-derived space group preferences as a function of predicted lattice energy, spontaneous resolution of chiral molecules and the training of transferable machine-learned energetic models from a very large set of CSP landscapes.