A common approach to including a physical baseline is with ∆-learning which, rather than learning total energies, focuses on learning the difference between a lower level method, such as a force field or Density Functional based Tight Binding (DFTB), and the higher level method, such as DFT. With the appropriate choice of the baseline, ∆learning combines the accurate description of long-range effects with the high-level accuracy of MLPs for short range interactions, which can increase the accuracy of the final model with less data. The validity of this approach has been demonstrated for organic crystals, with further extensions including multimer corrections and training separate models for the intramolecular and intermolecular components. Accurate MLPs are also highly dependent on the training data collected. This is because the non-physical functional form of MLPs means they are typically only accurate interpolating within regions of the energy surface covered by the training data. Consequently, generating comprehensive, representative, and diverse datasets is a non-trivial problem and a key concern for MLP development. On one hand, large datasets allow MLPs to cover a wide region of the energy landscape with high accuracy. On the other hand, large datasets are unfavorable because large numbers of expensive ab initio calculations are required and the computational cost of MLP training increases. Furthermore, large randomly sampled datasets might introduce biases towards common configurations. A common approach to avoid the pitfalls of a training set that is too small or too large is active learning, where the model itself iteratively selects structures to add to the training set from a large pool of candidate structures, yielding a final training set that reproduces the diversity of the full dataset with a minimal number of structures. Active learning has been found to significantly reduce the training set size required to achieve a certain level of accuracy of MLP, reducing computational costs proportionally. Because active learning cannot increase the scope of the MLP beyond that covered by the set of candidates, generating robust candidate datasets is still important. Molecular dynamics is one common method for generating these datasets. However, in the context of MLPs for solid state systems, CSP landscapes can provide more diverse candidates, covering a wide scope of the potential energy surface and largely free of selection biases. As well as improving the efficiency of future CSP studies, MLP training to CSP landscapes can leverage the excellent resource of already published CSP landscapes for those interested in developing MLPs for crystal structure modelling. In this contribution, we investigate how best to develop MLPs, specifically neural network potentials (NNPs), from organic CSP landscapes. We begin by examining active learning on a CSP landscape of oxalic acid (Fig. ), investigating the effects of hyperparameters and strategies on the size and quality of the selected training set. From this we identify an efficient approach combining active learning with ∆-learning. Thereafter, we demonstrate this approach on the CSP landscapes for triptycene-tris(benzimidazolone) (TTBI, Fig. ) and resorcinol (Fig. ), each containing thousands of structures, to the DFT level. Finally, we demonstrate extending these NNPs to structures far from the CSP minima by combining on-the-fly training with Monte-Carlo simulations. The scheme presented here provides access to MLPs relevant over a wide scope of the crystal packing space and with the exacting accuracy required for organic CSP in a simple, efficient, and highly automated workflow.