We address the differentially private estimation of multiple quantiles (MQ) of a dataset, a key building block in modern data analysis. We apply the recent non-smoothed Inverse Sensitivity (IS) mechanism to this specific problem and establish that the resulting method is closely related to the current state-ofthe-art, the JointExp algorithm, sharing in particular the same computational complexity and a similar efficiency. However, we demonstrate both theoretically and empirically that (non-smoothed) JointExp suffers from an important lack of performance in the case of peaked distributions, with a potentially catastrophic impact in the presence of atoms. While its smoothed version would allow to leverage the performance guarantees of IS, it remains an open challenge to implement. As a proxy to fix the problem we propose a simple and numerically efficient method called Heuristically Smoothed JointExp (HSJointExp), which is endowed with performance guarantees for a broad class of distributions and achieves results that are orders of magnitude better on problematic datasets.