The structure of crystalline molecular materials is crucial to applications ranging from pharmaceuticals to organic semi-conductors. Polymorphs, crystals of the same compound with different structures, are consequently of great interest as they can significantly alter the physical properties, either to enhance or diminish them. Over the past decades there has been considerable effort to improve our understanding of polymorphism. However, determining the accessible polymorphs of a given compound still relies mostly on screening crystallisations under a wide range of conditions, including different solvents, temperatures, additives, humidities, and pressures. Crystal structure prediction (CSP) has demonstrated potential to augment polymorph screens, indicating the propensity of a compound to exhibit polymorphism and directing experimental efforts towards conditions that favour specific proposed polymorphs. The aim of conventional CSP is to locate all thermodynamically stable structures of a given compound by searching the crystal packing space and energy minimizing the resulting structures to the nearest local minimum on the energy surface. The unique structures are then ranked by a fitness function, typically the lattice energy, to identify potentially observable polymorphs. Due to the thousands of putative structures required to sufficiently sample the high dimensional energy surface, the initial energy minimizations are generally at the force field level, though recently it has become common to further refine the rankings of the lowest energy structures through more accurate, but more costly, dispersion-corrected density functional theory (DFT+D) calculations. CSP results are often presented in an energies vs. densities (or other structural feature) plot with the assumption that the lowest energy structures correspond to potentially observable polymorphs. Typically, an energy cutoff from the global energy minimum is applied to identify the set of plausible polymorphs. The exact cutoff varies; however, studies using force fields fitted for organic crystals have shown that 95% of polymorphs are within 7.2 kJ mol , with many polymorphs being seperated by much smaller energies. A recurring pattern throughout the history of CSP is that many more polymorphs are predicted than are experimentally observed. This is observed for for simple systems, such as small rigid molecules, as well as larger, more complex molecules, and even those that have been subjected to extensive experimental polymorph screening. Furthermore, this overprediction has become a key limitation on applying CSP to materials discovery since it can suggest promising structures exist that in reality are not accessible, potentially wasting experimental resources. While a number of factors have been reasonably proposed to contribute to the overprediction, including neglecting crystallization kinetics and disorder, one of the better understood causes is in the lack of finite temperature effects. The static lattice energy surface that underlies conventional CSP effectively describes the system at 0 K, and therefore successful prediction relies on there being a one-to-one mapping between this energy surface and the free energy surface at the finite temperatures at which crystallization occurs experimentally. However, it is well known that the potential energy surface is typically much rougher than the free energy surface since including thermal energy allows minima separated by small energy barriers, on the order of kT, to coalesce into a single free energy basin. Hence, it has been found that free energy basins typically correspond not to a single potential energy minimum but rather an ensemble of minima with the size of the ensemble related to conformational flexibility and temperature. The prominence of overprediction in CSP has led to efforts to systematically reduce the number of candidate structures from the initial CSP landscape towards a smaller set of structures more likely to be observed experimentally. Methods based on arguments of crystallisation kinetics and packing similarity have been proposed. However, the most developed methodology involves a series of molecular dynamics (MD) and enhanced sampling simulations to group the CSP structures into free energy clusters. This method has a strong physical basis and has been successfully applied to a variety of systems. Nevertheless, the protocol has not become widely adopted primarily due to its complexity, both in the simulations and in the processing and analysis of the results. Furthermore, many of the studies reported are limited by using common MD force fields rather than the more elaborate and accurate energy models typically required for CSP. Consequently, the results are from a different energy surface than that of the original CSP leading to ambiguity regarding the connection between the two.