CSP methods can conceptually be divided into two parts: first, the high-dimensional lattice energy space is comprehensively sampled to identify all relevant low energy, stable structures; and thereafter, the structures are ranked in terms of how likely they are to be observed. In general, kinetics are neglected and the crystal structures are ranked based on thermodynamic stability. The resulting predicted landscapes typically contain hundreds to thousands of unique structures. As evidenced by the CSP blind tests, the best sampling methods have achieved maturity and reliably locate matches to experimental structures for rigid and moderately flexible molecules. By contrast, determining the relative energy ranking of predicted organic crystal structures remains a notable challenge with often hundreds of distinct structures being within the typical energy range of polymorphism ( ˜7-8 kJ mol -1 ) above the global energy minimum. Differentiating these structures requires accurately evaluating the subtle balance of weak intermolecular interactions that are highly important in organic crystals. High level ab initio calculations provide a measure of consistency in identifying the balance of intermolecular forces. However, these calculations have considerable computational cost and therefore are typically only applied to a subset of the predicted landscape, or are restricted to researchers with access to very large-scale computing resources.