The role of CSP must be to predict all likely crystal structures, along with a measure of their likelihood. There have been high profile cases in the pharmaceutical industry where failing to anticipate polymorphism of a drug had serious consequences. In one case, Rotigotine, a prescription for the treatment of Parkison's disease, began crystallizing to an unknown polymorph in transdermal patches . This new form was thermodynamically more stable than the original polymorph, less soluble, and thus less efficient. In another case, an unknown polymorph of Ritonavir, a drug for the treatment of HIV, was accidentally produced, which was considerably less soluble and thus bioavailable . As well as imposing substantial costs for the drug companies, the temporary removal of these drugs from the market also interrupt the pa- ‡ These authors contributed equally. a Statistical Sciences Research Institute, University of Southampton, Southampton, SO17 1BJ, United Kingdom b Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, SO17 1BJ, United Kingdom; E-mail: G.M.Day@soton.ac.uk. tients' treatment. CSP could form part of a strategy to minimize such risks, by guiding experiments for the preparation of undiscovered polymorphs or providing additional confidence that no such undiscovered polymorphs exist. Applications of CSP are also developing in the area of functional materials discovery by linking molecular structure to likely materials structures and, hence, to properties of interest; this process can be used to screen and prioritise potential synthetic targets. CSP is usually approached as a problem in global optimization. Possible crystal structures correspond to local minima on a high dimensional energy surface determined by the structural degrees of freedom defining a crystal structure (unit cell dimensions, molecular positions and orientations, and intramolecular degrees of freedom). The process of CSP can be conceptually split into exploration for putative crystal structures, followed by their ranking, usually based on calculated lattice energies. The global energy minimum is assumed to correspond to the most likely observable crystal structure. However, the prevalence of polymorphism demonstrates that other higher energy crystal structures are also important and the energetic range of observed polymorphism gives an indication of the region of the crystal energy landscape that will normally include all observable crystal structures. A study of over 500 pairs of known polymorphs revealed that over half of the pairs had a lattice energy difference of less than 2 kJ mol -1 , and only 5% had an energy difference higher than 7.2 kJ mol -1 . CSP has revealed that small organic molecules often have dozens, and sometimes over 100 possible crystal structures within this small energy range. A reason behind the plurality of low energy crystal structures is the weak nature of packing forces such as dispersion interactions, hydrogen bonding, and less specific polar interactions. For most molecules, there are many ways that these interactions can combine to form similarly low-energy crystal structures. The small differences in lattice energies between competing crystal structures imply that a very good energy resolution is needed for reliable energy rankings. This requires the compu-tation of crystal energies with a high level of theory, for which solid state, periodic implementations of density functional theory (DFT) have become a popular approach. The cost of solid state DFT is too high to use for the entire CSP process. This is because a thorough sampling of the lattice energy surface typically requires the generation and optimization of tens or hundreds of thousands of trial crystal structures. It is, therefore, more common to take a hierarchical approach, where crystal structures are initially generated and ranked using lower cost methods (e.g. force fields), followed by re-ranking of the lowest energy predicted crystal structures using DFT. Final energy calculations using generalized gradient approximation (GGA) exchange-correlation DFT functionals usually suffice for ranking of structures in a organic molecular CSP. However, there are sensitive cases that require energies at a higher level of theory, such as hybrid functional DFT. In these cases, energy evaluation can become prohibitively expensive, even for CSP of very small molecules. The situation becomes worse as the size of the molecule increases. A fast, accurate approach to achieve energies of equivalent quality to hybrid DFT would be highly desirable.