Boost the performance of partition 1:n into equivalence classes

I am trying to speed up the function that make partition of 1:n into equivalence classes according to boolfn. I need the partition to be sorted, so using SimplePartition.jl or DataStructures.jl may not be optimized (since disjoint set structure are not sorted, and later sorting caused a lot of overhead).

function computePartition(n::Int, boolfn)
    indices = collect(1:n)
    part = Vector{Int}[] # can this be optimized, knowing that length(part) <= n?
    it = 0
    while !isempty(indices)
        it += 1
        class = [indices[1]]
        append!(class, [j for j in indices[2:end] if boolfn(indices[1], j)]) # can this be speed up?
        push!(part, class)
        filter!(k->!(k in class), indices)
    return part

My algorithm is not parallelizable at the level of the while loop. However,

[j for j in indices[2:end] if boolfn(indices[1], j)]

may be speed up because the computation can be parallelized. Does Julia parallel the array comprehension?