In conclusion, we have presented an alternative application of the threshold algorithm for molecular crystals: reducing CSP overprediction. We demonstrated this method, termed threshold clustering, on three systems with varying intermolecular interactions, conformational flexibility, and energy models with the results showing that it can significantly reduce the number of candidate structures on CSP landscapes without discarding matches to experimental structures. While threshold clustering does not represent a singular solution for overprediction, and many of the factors that contribute to overprediction, in particular crystallisation kinetics, will still need to be addressed, we see threshold clustering as a valuable addition to the toolset for identifying observable polymorphs. We expect the reduced set of structures to combine synergistically with methods such as DFT+D calculations to more accurately determine energy rankings, dynamics simulations to probe thermal stability and thermal averaging, and rugosity calculations to estimate the relative ease of crystallisation. Ongoing studies are investigating a number of optimisations and improvements to the algorithm, including convergence criteria to improve sampling efficiency and more system-specific energy models, such as machine-learned and tailor-made force fields, to improve the accuracy of the underlying energy surface. Overall, we believe the commonalities with CSP methods and the modest cost to be key benefits of the method. Undoubtedly, advancing methods for reducing overprediction that are simple and more accessible is an essential step towards the ultimate objective of accurately predicting observable crystal structures.