Overall, the results presented for benzene, acrylic acid, and resorcinol demonstrate threshold clustering can meaningfully reduce overprediction in CSP landscapes. However, we do not expect that every structure in the resulting landscapes will be an observable polymorph. The accuracy of the threshold clustering will depend on the completeness of the sampling, as well as the accuracy of the energy model and thus the resulting estimated energy surface. Structures that are poorly ranked are likely to be clustered out, including experimental structures that are not the lowest energy minimum within their basin. Additionally, most CSP energy models, including those used here, yield potential energy surfaces. Consequently, compared to threshold clustering, a benefit of MD methods is that they inherently operate on the free energy surface. Of course, this is not strictly an issue with the threshold clustering algorithm and, assuming a suitable free energy model can be supplied, we expect the approach will work equally well. Beyond the energy model, an important consideration for threshold clustering is how the structures from the overlapping trajectories are clustered. In this study we simply clustered together all structures that could be reached through any series of transitions as long as each step in the path did not exceed the energy threshold. Besides the issue of how feasible a series of single-crystal to single-crystal transitions is, it is also possible to imagine, if trajectories are initiated from minima close to the top of the basin, a trajectory may climb out of the basin and into another. With the clustering used here, this would cause the two basins to be erroneously clustered into a single basin. The results detailed here, however, are not indicative of overclustering, and moreover, there are clear similarities between many of the clustered structures. We expect that this is due to the majority of the CSP minima sampled being relatively low in the basins, possibly reflecting an asymmetric area distribution among energy minima, wherein lower energy minima have larger basins of attraction than higher energy minima and so are more likely to be found by a uniform sampling of the energy surface. Nevertheless, if ergodicity is achieved from minima within the threshold energy to the top of the basin, the possibility of overclustering with the current clustering approach remains. To avoid this entirely will thus require either changing the clustering to only group structures within a specified energy from the lowest energy structure in each basin (higher energy structures that connect to the basin being discarded) or generating the initial CSP landscape with methods that target basin minima, such as basin-hopping and simulated annealing.