The prediction of crystal structures given only basic connectivity information of a molecule, such as a chemical diagram, is an important challenge for computational chemistry. The motivations for crystal structure prediction (CSP) are three-fold. Many molecules do not produce single crystals suitable for structure determination using conventional methods, and direct structure solution from powder X-ray diffraction patterns is not always possible. The computational generation of likely structures can hence provide a useful starting point for structure determination from incomplete data: examples include structure determination from powder X-ray diffraction , electron diffraction and solid state NMR 5 data. There is strong interest in CSP in the pharmaceutical industry, where different crystal forms of a molecule (polymorphs) can have different properties, so that the anticipation of polymorphism is important for maintaining strict property control , as well as having intellectual property implications . In a broader context, the prediction of crystal structures could have extensive ramifications for materials science. The reliable prediction of solid-state phases from molecular information creates an opportunity for in silico design, where materials with desirable properties can be identified before entering the laboratory . In addition to the economic benefits of guiding experimental work towards the most promising candidate molecules, a computation-led approach can enable the exploration of untouched areas of chemical space. CSP is typically approached as an exploration of the high dimensional lattice energy surface for low energy local minima. To exhaustively explore the space of possible crystalline forms, it is often necessary to generate tens or hundreds of thousands of trial crystal structures for a given molecule , and a metric is required for identifying structures that are of interest. In the most general case, candidate crystal structures are ranked by their geometry-optimized, static lattice energies, under the assumption that crystal structure is largely driven by thermodynamics . Hence, the lowest energy predicted structure is assumed to be the most likely experimental structure, and alternative structures within a small lattice energy range are assumed to be potential polymorphs. Calculating accurate lattice energies for large numbers of crystal structures is one of the key challenges in CSP, due to the computational expense of calculating sufficiently accurate energies to rank predicted crystal structures: the lowest energy predicted crystal structures, and pairs of observed polymorphs, are often separated by less than a kJ/mol. For organic molecular crystals, the two most widely adopted approaches to ranking predicted structures are anisotropic, multipole-based atom-atom force fields , and dispersion-corrected periodic Density Functional Theory (DFT-D) methods .